The report may focus on how B2C marketers can make use of AI but anyone considering an AI platform for data analytics can learn from the five AI myths Forrester attempts to dispel.

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Myth 1: AI is new technology for marketers

Not true, says Forrester. In reality, demand-side platforms (DSPs) have been applying machine learning techniques to programmatic real-time bidding (RTB) for years. They're doing it on their end, though, so all you see are optimized ad campaigns.

Outside of marketing, those interested in using machine learning to accomplish a task should always look around to see who's offering it—just because someone says they're the only one on the market doesn't mean they're actually offering something unique.

Myth 2: AI is all about the algorithm

Nope—it's the data. AI's current strength is in its ability to analyze data and find connections that humans miss. The best algorithm in the world can't do much with data that's sparse, poorly organised, or inaccurate.

Don't trust anyone who says their algorithm can do more with your data—if you give them the same stuff you give someone else you'll get the same results.

Myth 3: AI platforms work out of the box

That shiny new learning machine you just set up isn't ready for the big time: It's a new mind with the framework for complex problem solving but none of the experience it needs to draw conclusions.

AIs try to emulate human cognition, and in order to do so they have to be trained. Forrester reports that it can take weeks to organize data, days to create training models, and up to six months to fully optimize algorithms.

In other words don't expect immediate results.

Myth 4: AI autonomy will kill jobs that rely on it

Not so, at least according to the report. What AI does (both for marketers and other data-driven professions) is free up time spent on minutiae. Let machine learning do all the number crunching, data compiling, and report generating so your human employees can close sales and create content.

Myth 5: AI findings will be rich with customer insights

Learning machines don't care about what they learn and they don't understand a bit of it. That means that while an AI may discover a trend between two previously unconnected data points it has no idea how to break it down into a real, intelligent insight.

Drawing insights from data is left up to us humans, and in many cases we can only hypothesize on the connection. Asking the AI is useless—it's not smart enough to know what the data it processes represents, and it's not human enough to care about it either.

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